OPES

An Open-source Portfolio Estimation System for advanced portfolio optimization and backtesting.

Overview

OPES is a comprehensive Python library for advanced portfolio optimization and backtesting. It is designed for quantitative finance enthusiasts who wants to explore cutting-edge methods for portfolio research or understanding. OPES aims to be research first, trying to prioritize sheer breadth over depth for each paradigm. OPES aims provide a wide range of portfolio strategies, risk measures and robust evaluation tools for users to always stay updated with current portfolio optimization techniques. OPES's inspiration grew from my own project, MOP, along with the giants of portfolio optimization: PyPortfolioOpt and Riskfolio-Lib.

Visit the documentation for detailed insights on OPES.

Project Methodology

This project follows an Agile development approach. Every feature is designed to be extensible, exploratory and open to modification as the system evolves. Each GitHub commit represents a usable and coherent version of OPES. While not every commit is feature-complete or fully refined, each serves as a stable minimum viable product and a reliable snapshot of progress. Features marked as experimental are subject to active evaluation and will be either validated and promoted or removed entirely based on feasibility and empirical performance.

Disclaimer

The information provided by OPES is for educational, research and informational purposes only. It is not intended as financial, investment or legal advice. Users should conduct their own due diligence and consult with licensed financial professionals before making any investment decisions. OPES and its contributors are not liable for any financial losses or decisions made based on this content. Past performance is not indicative of future results.

Portfolio Objectives

Metrics

Other Features

Installation

Using pip

If you have pip, it is very convenient to install opes.

pip install opes

From the Source

Alternatively, you are also welcome to install directly from the GitHub repository.

git clone https://github.com/opes-core/opes.git
cd opes-main
pip install .

You can also install in editable mode if you plan on making any changes to the source code.

# After cloning
pip install -e .

Verification

Verify your installation by using pip.

pip show opes

You can also verify by using python.

>>> import opes
>>> opes.__version__

Getting Started

opes is designed to be minimalistic and easy to use and learn for any user. Here is an example script which implements my favorite portfolio, the Kelly Criterion.

# I recommend you use yfinance for testing.
# However for serious research, using an external, faster API would be more fruitful.
import yfinance as yf

# Importing our Kelly class
from opes.objectives import Kelly

# Obtaining ticker data
# Basic yfinance stuff
TICKERS = ["AAPL", "NVDA", "PFE", "TSLA", "BRK-B", "SHV", "TLT"]
asset_data = yf.download(
    tickers=TICKERS, 
    start="2010-01-01", 
    end="2020-01-01", 
    group_by="ticker", 
    auto_adjust=True
)

# Initialize a Kelly portfolio with fractional exposure and L2 regularization
# Fractional exposure produces less risky weights and L2 regularization contributes in penalizing concentration
kelly_portfolio = Kelly(fraction=0.8, reg="l2", strength=0.001)

# Compute portfolio weights with custom weight bounds
kelly_portfolio.optimize(asset_data, weight_bounds=(0.05, 0.8))

# Clean negligible allocations
cleaned_weights = kelly_portfolio.clean_weights(threshold=1e-6)

# Output the final portfolio weights
print(cleaned_weights)

This showcases the simplicity of the module. However there are far more diverse features you can still explore. If you're looking for more examples, preferably some of them with "context", I recommend you check out the examples page within the documentation.

Testing

Tests for opes are written using the pytest module. You can run the tests easily using the following command.

cd project-root    # Navigate to project root
pytest

This will run three scripts, each dedicated to testing the optimizer, regularizer and backtesting engine. Note that the tests are heavy and can take a significant amount of time since it tests each of the available objectives. We test using a year's data for the following tickers.

GOOG, AAPL, AMZN, MSFT

The price data is stored in the prices.csv file within the tests/ directory. The number of tickers are limited to 4 since there are computationally heavy portfolio objectives (like UniversalPortfolios) included which may take an eternity to test well using multiple tickers.